ABSTRACT
Aims
This study aimed to investigate the global genetic diversity, evolutionary relationships, and antimicrobial resistance (AMR) profiles of Klebsiella pneumoniae by performing a comprehensive pan-genome and phylogenetic analysis across worldwide isolates.
Materials and methods
A total of 72,057 K. pneumoniae genomes were retrieved from the NCBI database, from which 91 high-quality representative genomes each from a unique country were selected based on completeness, metadata availability, and sequence quality. Genomic assemblies were assessed using QUAST, annotated with PROKKA, and analyzed for pan-genomic composition and phylogenetic relatedness using standard bioinformatics pipelines.
Results
The pan-genome revealed a large accessory component, reflecting extensive genomic plasticity and adaptability. QUAST analysis indicated significant variability in genome size and contig number, while PROKKA annotation identified diverse coding sequences, tRNA, rRNA, and AMR genes. Phylogenetic clustering demonstrated both geographically localized and globally disseminated lineages, suggesting regional adaptation and intercontinental transmission.
Conclusions
This study provides a global perspective on the genomic diversity and evolutionary patterns of K. pneumoniae. The widespread presence of AMR determinants underscores the urgent need for continuous genomic surveillance and integration of metagenomic approaches to improve monitoring, infection control, and therapeutic strategies against multidrug-resistant strains.
KEYWORDS: K. pneumoniae, AMR, global genomic surveillance, genomic diversity, mobile genetic elements
1. Introduction
K. pneumoniae was primarily identified by Carl Friedlander in 1882, when isolating the encapsulated bacterium from the lungs of pneumonia patients [1]. Previously, it was known as Friedlander’s bacillus. Later, it was renamed K. pneumoniae in 1886. This gram-negative, encapsulated, non-motile bacterium is commonly found in the environment and can cause pneumonia in individuals with alcohol abuse or diabetes [2]. As a bacillus from the Enterobacteriaceae family, K. pneumoniae is a common part of the human gut microbiome but has gained recognition as an invasive pathogen, especially in healthcare environment [3]. This bacterium causes numerous diseases including pneumonia, urinary tract infections, bloodstream infections, and liver abscesses, particularly affecting immunocompromised individuals [4]. Human beings are the main reservoir for K. pneumoniae, with 5% to 38% of the population carrying it in their feces and 1% to 6% in their nasopharynx. In healthcare facilities, the patient’s gastrointestinal tract and the hands of medical personnel are primary reservoirs of infection, often leading to nosocomial outbreaks [2,5].
K. pneumoniae is known for its thick polysaccharide capsule, which not only contributes to its mucoid appearance but also plays a crucial role in evading host immune defenses [6]. K. pneumoniae exhibits significant capsular diversity, which contributes to its adaptability and pathogenicity in various host environments [7]. In addition to its increasing drug resistance.
The therapeutic importance of K. pneumoniae has significantly increased in past few years owing to its exceptional capacity to obtain antibiotic resistance mechanisms. The increase of carbapenem-resistant K. pneumoniae (CRKP) strains is particularly concerning due to their limited treatment options and high mortality rates [8]. The study by Sanchez et al. [9] finds that the surveillance from 1998–2010 total of 3,132,354 susceptibility findings showed significant increases in resistance to most antimicrobial agents, except tetracycline, indicating a concerning trend in medicine efficacy against the virus. This suggested cross resistance among CRKP, which was found to be lower for tetracycline and amikacin [9]. Additionally, Li et al. [10] showed pathogenic K. pneumoniae isolates, including hypermucoviscous and specific serotypes, with potential drug resistance and emergence of a carbapenem-resistant hypervirulent strain [10]. Recent reports have highlighted a global rise in CRKP) following the COVID-19 pandemic, indicating increased hospital transmission and antimicrobial pressure across regions [11]. In parallel, the emergence of hypervirulent K. pneumoniae (hvKP) strains, characterized by enhanced virulence and poor clinical outcomes, has become a major global concern [12]. The convergence of antimicrobial resistance (AMR) and hypervirulence in K. pneumoniae poses a critical threat to infection control and therapeutic management, emphasizing the need for genomic surveillance and early detection strategies.
In recent years, pan-genome analysis has emerged as a crucial tool for comprehending the genomic structure of bacterial species like K. pneumoniae [13]. This approach allows researchers to distinguish between core genes, which are common to all strains, and accessory genes, which are specific to individual isolates and often confer adaptive advantages such as antibiotic resistance or enhanced virulence [14]. These studies suggested the need for genetic insight, which is where pan-genome analysis becomes valuable.
As multidrug-resistant K. pneumoniae clones continue to spread globally, there is growing concern about their impact. A comprehensive study is crucial to understand this threat. Investigating the genetic diversity and population structure of the pathogen worldwide reveals important details about antibiotic resistance, virulence, and its ability to adapt to different environments. Therefore, the current study aimed to conduct a comprehensive pan-genome analysis and construct a phylogenetic tree of K. pneumoniae strains from diverse geographical regions. By analyzing 91 genomes, each representing a distinct country, the research pursued to investigate the genomic heterogeneity and evolutionary relationships within this species on a global scale. The study employed a suite of bioinformatics tools, for genome assembly quality assessment and functional annotation, and Roary for pan-genome analysis.
2. Materials and methods
2.1. Retrieval of K. pneumoniae genomes
The genomic data for K. pneumoniae strains were obtained from the National Centre for Biotechnology Information (NCBI) database [15]. The metadata of 72,057 K. pneumoniae genomes retrieved included accession number, assembly name, assembly type, collection date, geographic location, and total sequence length. These records were systematically filtered to ensure the inclusion of only high-quality and representative genomes. The filtering criteria comprised the following: (i) only assemblies labeled as “haploid” or “complete genome” were considered; (ii) genomes with abnormally short or excessively long total sequence lengths compared to the expected K. pneumoniae genome size (5.2–5.7 Mbp) were excluded; (iii) the most recent collection date from each country was selected to represent current genetic diversity; (iv) genomes lacking valid geographic metadata (geo_loc_name) or collection year were excluded; and (v) duplicates or assemblies with incomplete metadata were removed. After applying these filters, 91 high-quality genomes, each representing a unique country, were retained for subsequent comparative genomic and phylogenetic analyses.
2.2. Genome quality assessment using QUAST
The quality of the assembled genomes was evaluated using the Quality Assessment Tool for Genome Assemblies (QUAST) [16]. This approach ensured that the selected genomes that will be used for further investigation were high in quality, free from contamination, and exhibited acceptable assembly metrics. QUAST provided critical parameters such as N50 values, total genome length, contig count, and GC content, facilitating the identification of low-quality genomes to ensure the reliability of comparative genomic analyses.
2.3. Genome annotation with PROKKA
Genome annotation is essential for identifying coding sequences (CDS) and genomic structure of the studied genomes. The selected genomes were annotated using the PROKKA program [17]. The selected genomes were annotated using the PROKKA tool, which identified protein-coding genes, ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and other genomic elements. The PROKKA results provided data on the number of CDS, ribosomal RNA genes, tRNA genes, and overall genomic characteristics for each K. pneumoniae strain.”
The AMR gene profiles were derived directly from the annotation results generated by the PROKKA pipeline. PROKKA incorporates reference datasets such as the ResFinder and ARG-ANNOT databases to annotate known resistance determinants. The AMR-related annotations obtained through PROKKA were filtered to retain only those with high-confidence functional assignments. Each genome’s annotation file was systematically parsed to extract AMR-related coding sequences (CDS), and the total count of these genes was compiled for comparative analysis. Although the AMR gene prediction relied on computational annotation rather than re-analysis with recent resistance databases, it provided a consistent framework for assessing the relative abundance and diversity of AMR determinants across the 91 genomes.
2.4. Pangenome analysis with roary
A pangenomic analysis was performed to identify the core and accessory genes present in the 91 genomes. The roary pangenome program was utilized to classify genes into orthologous groups across the genomes [18]. Roary generated a gene presence/absence matrix that outlined the distribution of gene clusters among all strains, enabling the categorization of genes as either core (present in all genomes) or accessory (present in a subset of genomes). Phylogenetic analysis relied on the genomic alignment that Roary generated.
2.5. Phylogenetic analysis and tree construction
A phylogenetic analysis was conducted using the core genomic alignment produced by Roary to investigate the evolutionary relationships among 91 K. pneumoniae genomes. The phylogenetic tree was constructed using FastTree [19], which employs a maximum likelihood approach to infer evolutionary relationships from large genomic datasets. The whole phylogenetic tree was illustrated to clearly represent the evolutionary relationships among the K. pneumoniae genomes. The Interactive Tree of Life (iTOL) program [20] facilitated detailed customization and annotation for visualization purposes. The phylogenetic tree was arranged in a circular format to allow for passable viewing of all 91 strains. Each strain was labeled with its genomic accession number and country of origin.
3. Results
3.1. Retrieval of K. pneumoniae genomes
The present study retrieved a total of 72,057 genomes of K. pneumoniae, that were primarily obtained from the NCBI database. After collecting these data, the data were sorted as per the most current sample from each nation. The mentioned process resulted in the further selection of 91 genomes, each representing a different country. These 91 genomes were listed in Table 1 based on their collection date and the country of occurrence. The genomes exhibited extensive geographic distribution, including countries from Africa, Asia, Europe, North America, South America, and Oceania. Countries such as the United States, United Kingdom, China, and India were prominently represented, demonstrating the global distribution of K. pneumoniae. Figure 1 illustrates a world map used to visually convey the global distribution of the selected genomes, focusing on the countries that contributed to the genome dataset. Additionally, the map demonstrates the widespread occurrence of K. pneumoniae and provides a foundation for subsequent genetic analysis. The large set of genomes gathered from various continents ensure a thorough understanding of the pathogen’s genetic variance, simplifying further examination of genomic diversity and geographic clustering patterns.
Table 1.
Summary of Klebsiella pneumoniae genome retrieval by country and collection date from NCBI.
| Accession | Assembly | Collection date | Country | Sequence Length |
|---|---|---|---|---|
| GCA_030845575.1 | ASM3084557v1 | 2018 | Afghanistan | 5737974 |
| GCF_006151695.1 | ASM615169v1 | 2018 | Algeria | 5823864 |
| GCF_037449135.1 | ASM3744913v1 | 2022 | Argentina | 5540031 |
| GCA_030294165.1 | ASM3029416v1 | 2021 | Australia | 5594801 |
| GCA_003583095.1 | ASM358309v1 | 2016 | Austria | 5696674 |
| GCF_036235415.1 | ASM3623541v1 | 2023 | Bangladesh | 5361640 |
| GCA_020005265.1 | ASM2000526v1 | 2020 | Belarus | 5425462 |
| GCF_036950405.1 | ASM3695040v1 | 2019 | Belgium | 5456468 |
| GCA_038433245.1 | ASM3843324v1 | 2023 | Brazil | 5567630 |
| GCF_963854595.1 | KP31122023 | 2023 | Bulgaria | 5820489 |
| GCA_022175105.1 | PDT000750133.1 | 2017 | Cambodia | 5316747 |
| GCA_026323435.1 | PDT001495069.1 | 2020 | Canada | 5698674 |
| GCF_032253935.1 | ASM3225393v1 | 2022 | Chile | 5615879 |
| GCA_041023815.1 | ASM4102381v1 | 2024 | China | 5764993 |
| GCF_036668115.1 | ASM3666811v1 | 2023 | Colombia | 5735378 |
| GCA_028614935.1 | ASM2861493v1 | 2020 | Croatia | 4772599 |
| GCA_016939815.1 | ASM1693981v1 | 2019 | Czech Republic | 5698780 |
| GCA_041020085.1 | ASM4102008v1 | 2020 | Czechia | 5629108 |
| GCA_028994575.1 | ASM2899457v1 | 2023 | Denmark | 6051092 |
| GCA_029955815.1 | ASM2995581v1 | 2022 | Ecuador | 5351959 |
| GCA_031174725.1 | PDT001873519.1 | 2021 | Egypt | 5774152 |
| GCA_024479345.1 | PDT001370401.1 | 2020 | Ethiopia | 5681276 |
| GCF_040137595.1 | ASM4013759v1 | 2023 | France | 5509846 |
| GCA_027908695.1 | PDT001551536.1 | 2022 | Germany | 5892959 |
| GCA_022968285.1 | ASM2296828v1 | 2021 | Ghana | 5666331 |
| GCA_041361325.1 | ASM4136132v1 | 2024 | Greece | 5765284 |
| GCA_024376135.1 | PDT001366188.1 | 2018 | Guadeloupe | 5510566 |
| GCA_022316305.1 | PDT000111518.2 | 2012 | Honduras | 5811065 |
| GCF_037167165.1 | ASM3716716v1 | 2023 | Hong Kong | 5549664 |
| GCF_032807725.1 | ASM3280772v1 | 2023 | Hungary | 5743644 |
| GCF_041679105.1 | ASM4167910v1 | 2024 | India | 5530989 |
| GCF_019433925.1 | ASM1943392v1 | 2019 | Iran | 5680506 |
| GCA_017916215.1 | ASM1791621v1 | 2015 | Iraq | 5201616 |
| GCA_022194705.1 | PDT000715124.1 | 2011 | Ireland | 5765626 |
| GCA_024499835.1 | ASM2449983v1 | 2022 | Israel | 5636227 |
| GCA_036421895.1 | ASM3642189v1 | 2023 | Italy | 5707905 |
| GCA_030878345.1 | PDT001701438.1 | 2020 | Japan | 5519986 |
| GCA_037053885.1 | ASM3705388v1 | 2017 | Jordan | 5561739 |
| GCA_019915325.1 | ASM1991532v1 | 2021 | Kazakhstan | 5576938 |
| GCA_037628085.1 | ASM3762808v1 | 2022 | Kenya | 5431963 |
| GCA_036881205.1 | ASM3688120v1 | 2016 | Kuwait | 5552261 |
| GCA_022207725.1 | PDT000651023.1 | 2015 | Laos | 5272122 |
| GCF_032676305.1 | ASM3267630v1 | 2023 | Lebanon | 5304825 |
| GCA_026329135.1 | PDT001494582.1 | 2020 | Lithuania | 5630027 |
| GCA_041363805.1 | ASM4136380v1 | 2022 | Madagascar | 5368848 |
| GCA_019877925.1 | ASM1987792v1 | 2016 | Malawi | 5525629 |
| GCF_036903135.1 | ASM3690313v1 | 2021 | Malaysia | 5404900 |
| GCF_037104535.1 | ASM3710453v1 | 2023 | Mexico | 5410133 |
| GCA_900514015.1 | 18174_5#79 | 2013 | Montenegro | 5358822 |
| GCA_026332835.1 | PDT001494626.1 | 2020 | Mozambique | 5610601 |
| GCA_022283225.2 | PDT000431898.2 | 2016 | Myanmar | 5601929 |
| GCA_041061405.1 | ASM4106140v1 | 2023 | Nepal | 5965341 |
| GCF_041023835.1 | ASM4102383v1 | 2023 | Netherlands | 5413684 |
| GCA_031051475.1 | PDT001716702.1 | 2015 | New Zealand | 5471831 |
| GCA_026326255.1 | PDT001494799.1 | 2020 | Nigeria | 5203566 |
| GCA_041863025.1 | ASM4186302v1 | 2018 | Norway | 5543151 |
| GCA_021875435.1 | PDT001197729.1 | 2020 | Oman | 6154406 |
| GCF_032842255.1 | 9055 | 2023 | Pakistan | 5287542 |
| GCA_026305215.1 | PDT001493239.1 | 2020 | Paraguay | 5421801 |
| GCF_040687995.1 | ASM4068799v1 | 2021 | Peru | 5898946 |
| GCA_002108355.1 | ASM210835v1 | 2013 | Philippines | 5454384 |
| GCA_037082035.1 | ASM3708203v1 | 2021 | Poland | 5522263 |
| GCF_032461365.1 | ASM3246136v1 | 2022 | Portugal | 5550563 |
| GCA_027894895.1 | PDT001557824.1 | 2021 | Qatar | 5529043 |
| GCA_009710645.1 | ASM971064v1 | 2019 | Romania | 5943681 |
| GCF_040539535.1 | ASM4053953v1 | 2024 | Russia | 5730640 |
| GCA_037206085.1 | ASM3720608v1 | 2023 | Saudi Arabia | 5722556 |
| GCF_030414195.1 | ASM3041419v1 | 2018 | Senegal | 5551901 |
| GCF_029848135.1 | ASM2984813v1 | 2017 | Singapore | 5157567 |
| GCA_964254225.1 | 24–2119 | 2024 | Slovakia | 5471848 |
| GCF_037004625.1 | ASM3700462v1 | 2022 | South Africa | 5740521 |
| GCF_033898565.1 | ASM3389856v1 | 2022 | South Korea | 5389891 |
| GCF_040763125.1 | ASM4076312v1 | 2022 | Spain | 5592654 |
| GCF_017898205.1 | ASM1789820v1 | 2015 | Sri Lanka | 5444822 |
| GCA_022530725.1 | ASM2253072v1 | 2021 | Sudan | 5675175 |
| GCF_029929895.1 | ASM2992989v1 | 2021 | Sweden | 6051072 |
| GCF_023612395.1 | ASM2361239v1 | 2019 | Switzerland | 5762752 |
| GCA_039724115.1 | ASM3972411v1 | 2022 | Taiwan | 5515513 |
| GCA_009925385.1 | ASM992538v1 | 2018 | Tanzania | 5481532 |
| GCF_024705635.1 | ASM2470563v1 | 2021 | Thailand | 5761774 |
| GCF_025819055.1 | ASM2581905v1 | 2016 | Trinidad and Tobago | 5592304 |
| GCF_002140135.1 | ASM214013v1 | 2015 | Tunisia | 5607827 |
| GCA_023026145.1 | PDT001289577.1 | 2019 | Turkey | 5688171 |
| GCA_033336495.1 | ASM3333649v1 | 2022 | Uganda | 5181822 |
| GCF_009887895.1 | ASM988789v1 | 2014 | Ukraine | 5637323 |
| GCA_014433645.1 | ASM1443364v1 | 2013 | United Arab Emirates | 6719317 |
| GCA_033253045.1 | ASM3325304v1 | 2023 | United Kingdom | 5923719 |
| GCA_024612745.1 | PDT001382059.1 | 2017 | Uruguay | 5572642 |
| GCA_041857965.1 | PDT002366229.1 | 2024 | USA | 5637052 |
| GCA_041400745.1 | ASM4140074v1 | 2021 | Viet Nam | 5765774 |
| GCA_026303475.1 | PDT001492934.1 | 2020 | West Bank | 5390763 |
Figure 1.
The most recent genomic data for each place was obtained from the 91 nations listed in orange. The global impact of the virus is underscored by this visualization, which displays the extensive dispersion of Klebsiella pneumoniae across varied geographic locations, spanning multiple continents.
An earlier study showed that a total of 245 K. pneumoniae genomes found positive for pks (clb gene island) [21] were found among 2709 genomes [22]. Out of these, 157 were sourced from the China National GeneBank Database (CNGBdb) [23] and 88 were found in the public database NCBI. Similarly, another study retrieved data from NCBI and obtained 373 full genomes and chromosomes for core genome extraction [24]. A total of 39 common K. pneumoniae strains were included in the small group sample for phylogenetic analysis. This sample included strains exhibiting pathogenicity, nitrogen fixing skills, and potential for industrial use. Another previous work found 157 lineages using whole-genome sequencing of a broad collection of 289 Kp genomes [25]. The public cgMLST database has 155 established CGs. The detection rate of new lineages indicates that the true number of existing lineages is probably in the thousands, greatly exceeding the established count. Thus, in this current study the genomes of K. pneumoniae were collected from various nations to provide significant contemporary, significant insights on the worldwide and evolutionary spread of K. pneumoniae.
3.2. Assembly quality assessment
The genome assembly qualities were evaluated by employing QUAST to analyze vital metrics, including total genome length, GC content, N50 values, and the number of contigs exceeding 1000 bp. The outcome of the QUAST observed in Table 2 highlights the overall quality and consistency of the assemblies for the 91 K. pneumoniae genomes. The total genome lengths ranged from 5.4 Mbp to 5.5 Mbp, consistent with the expected size for K. pneumoniae. The GC content among all genomes was uniform, ranging from 55.5% to 57.5%, with the majority of values concentrated around 57%, a trait typical of this species. This uniformity in GC content is a hallmark of this species’ genomic stability and confirms the reliability of the assemblies. The average GC content of K. pneumoniae was found to be around 58% in a prior study [24]. While the current study found a slightly lower GC content, it remains within the typical range observed for this species, accounting for possible strain variation.
Table 2.
Genome Assembly Statistics from QUAST Analysis for 91 Klebsiella pneumoniae Genomes.
| Accession | Assembly | Country | Total length | contigs | GC (%) | N50 | L50 |
|---|---|---|---|---|---|---|---|
| GCA_030845575.1 | ASM3084557v1 | Afghanistan | 5737974 | 4 | 56.94 | 5340491 | 1 |
| GCF_006151695.1 | ASM615169v1 | Algeria | 5823864 | 170 | 56.86 | 122891 | 15 |
| GCF_037449135.1 | ASM3744913v1 | Argentina | 5530195 | 5 | 57.23 | 154589 | 5 |
| GCA_030294165.1 | ASM3029416v1 | Australia | 5594801 | 3 | 57.14 | 5420112 | 1 |
| GCA_003583095.1 | ASM358309v1 | Austria | 5617365 | 223 | 57.2 | 357940 | 5 |
| GCF_036235415.1 | ASM3623541v1 | Bangladesh | 5356714 | 80 | 57.18 | 371239 | 6 |
| GCA_020005265.1 | ASM2000526v1 | Belarus | 4594873 | 3074 | 54.77 | 1909 | 651 |
| GCF_036950405.1 | ASM3695040v1 | Belgium | 5456468 | 91 | 57.1 | 191227 | 12 |
| GCA_038433245.1 | ASM3843324v1 | Brazil | 5566354 | 106 | 57.28 | 141399 | 14 |
| GCF_963854595.1 | KP31122023 | Bulgaria | 5352450 | 6 | 57.15 | 5589618 | 1 |
| GCA_022175105.1 | PDT000750133.1 | Cambodia | 5316747 | 84 | 57.46 | 198055 | 8 |
| GCA_026323435.1 | PDT001495069.1 | Canada | 5693556 | 250 | 57.08 | 49481 | 34 |
| GCF_032253935.1 | ASM3225393v1 | Chile | 5615879 | 3 | 56.85 | 5334759 | 1 |
| GCA_041023815.1 | ASM4102381v1 | China | 5755120 | 116 | 57.01 | 160332 | 13 |
| GCF_036668115.1 | ASM3666811v1 | Colombia | 5726944 | 103 | 57.11 | 129808 | 15 |
| GCA_028614935.1 | ASM2861493v1 | Croatia | 4513170 | 1292 | 56.47 | 4726 | 281 |
| GCA_016939815.1 | ASM1693981v1 | Czech Republic | 5698780 | 5 | 57.17 | 5312419 | 1 |
| GCA_041020085.1 | ASM4102008v1 | Czechia | 5620465 | 66 | 57.09 | 374649 | 6 |
| GCA_028994575.1 | ASM2899457v1 | Denmark | 6051092 | 4 | 56.51 | 5410656 | 1 |
| GCA_029955815.1 | ASM2995581v1 | Ecuador | 5344542 | 36 | 57.45 | 322222 | 6 |
| GCA_031174725.1 | PDT001873519.1 | Egypt | 5768371 | 259 | 56.98 | 51822 | 34 |
| GCA_024479345.1 | PDT001370401.1 | Ethiopia | 5668320 | 147 | 57.22 | 94039 | 18 |
| GCF_040137595.1 | ASM4013759v1 | France | 5509590 | 133 | 57.13 | 5235716 | 1 |
| GCA_027908695.1 | PDT001551536.1 | Germany | 5889652 | 112 | 56.78 | 186206 | 11 |
| GCA_022968285.1 | ASM2296828v1 | Ghana | 5661211 | 86 | 56.74 | 205953 | 9 |
| GCA_041361325.1 | ASM4136132v1 | Greece | 5757733 | 91 | 57.02 | 180988 | 11 |
| GCA_024376135.1 | PDT001366188.1 | Guadeloupe | 5510566 | 68 | 57.2 | 213862 | 9 |
| GCA_022316305.1 | PDT000111518.2 | Honduras | 5811065 | 97 | 57.18 | 220631 | 10 |
| GCF_037167165.1 | ASM3716716v1 | Hong Kong | 5546161 | 120 | 56.78 | 355645 | 16 |
| GCF_032807725.1 | ASM3280772v1 | Hungary | 5743644 | 2 | 57.19 | 5515480 | 1 |
| GCF_041679105.1 | ASM4167910v1 | India | 5525870 | 76 | 57.28 | 227165 | 6 |
| GCF_019433925.1 | ASM1943392v1 | Iran | 5680506 | 100 | 56.85 | 157973 | 12 |
| GCA_017916215.1 | ASM1791621v1 | Iraq | 5201616 | 1 | 57.52 | 5201616 | 1 |
| GCA_022194705.1 | PDT000715124.1 | Ireland | 5763402 | 100 | 57.15 | 224849 | 8 |
| GCA_024499835.1 | ASM2449983v1 | Israel | 5633714 | 80 | 56.84 | 252604 | 8 |
| GCA_036421895.1 | ASM3642189v1 | Italy | 5700989 | 80 | 56.76 | 175537 | 11 |
| GCA_030878345.1 | PDT001701438.1 | Japan | 5517065 | 63 | 57.23 | 372708 | 6 |
| GCA_037053885.1 | ASM3705388v1 | Jordan | 5554088 | 99 | 57.11 | 122560 | 14 |
| GCA_019915325.1 | ASM1991532v1 | Kazakhstan | 5576938 | 3 | 57.2 | 5319600 | 1 |
| GCA_037628085.1 | ASM3762808v1 | Kenya | 5426547 | 118 | 57.35 | 100849 | 18 |
| GCA_036881205.1 | ASM3688120v1 | Kuwait | 5540385 | 69 | 57.1 | 355747 | 5 |
| GCA_022207725.1 | PDT000651023.1 | Laos | 5272122 | 115 | 57.44 | 115752 | 14 |
| GCF_032676305.1 | ASM3267630v1 | Lebanon | 5290730 | 27 | 57.02 | 502078 | 4 |
| GCA_026329135.1 | PDT001494582.1 | Lithuania | 5629674 | 77 | 57.21 | 214696 | 9 |
| GCA_041363805.1 | ASM4136380v1 | Madagascar | 5367643 | 395 | 57.28 | 21983 | 77 |
| GCA_019877925.1 | ASM1987792v1 | Malawi | 5507460 | 157 | 57.06 | 100621 | 16 |
| GCF_036903135.1 | ASM3690313v1 | Malaysia | 5400979 | 63 | 57.24 | 226052 | 9 |
| GCF_037104535.1 | ASM3710453v1 | Mexico | 5405867 | 38 | 57.39 | 347819 | 12 |
| GCA_900514015.1 | 18174 | Montenegro | 5820489 | 6 | 57.28 | 345343 | 5 |
| GCA_026332835.1 | PDT001494626.1 | Mozambique | 5591568 | 407 | 57.3 | 29478 | 59 |
| GCA_022283225.2 | PDT000431898.2 | Myanmar | 5600218 | 68 | 57.3 | 414034 | 6 |
| GCA_041061405.1 | ASM4106140v1 | Nepal | 5965341 | 8 | 56.86 | 5467994 | 1 |
| GCF_041023835.1 | ASM4102383v1 | Netherlands | 5410997 | 97 | 56.92 | 191072 | 10 |
| GCA_031051475.1 | PDT001716702.1 | New Zealand | 5470173 | 78 | 57.47 | 260669 | 8 |
| GCA_026326255.1 | PDT001494799.1 | Nigeria | 5200319 | 196 | 57.63 | 48171 | 36 |
| GCA_041863025.1 | ASM4186302v1 | Norway | 5543151 | 5 | 57.01 | 5314429 | 1 |
| GCA_021875435.1 | PDT001197729.1 | Oman | 6154406 | 146 | 56.57 | 146525 | 15 |
| GCF_032842255.1 | 9055 | Pakistan | 5280415 | 67 | 57.66 | 174292 | 11 |
| GCA_026305215.1 | PDT001493239.1 | Paraguay | 5417522 | 181 | 57.34 | 55416 | 32 |
| GCF_040687995.1 | ASM4068799v1 | Peru | 5898946 | 3 | 57.3 | 5456495 | 1 |
| GCA_002108355.1 | ASM210835v1 | Philippines | 5454384 | 79 | 57.07 | 225222 | 9 |
| GCA_037082035.1 | ASM3708203v1 | Poland | 5522263 | 4 | 57.33 | 5242798 | 1 |
| GCF_032461365.1 | ASM3246136v1 | Portugal | 5539426 | 98 | 57.13 | 167970 | 11 |
| GCA_027894895.1 | PDT001557824.1 | Qatar | 5521710 | 123 | 57.25 | 104964 | 16 |
| GCA_009710645.1 | ASM971064v1 | Romania | 5898537 | 150 | 56.56 | 216943 | 8 |
| GCF_040539535.1 | ASM4053953v1 | Russia | 5730640 | 8 | 57.26 | 119523 | 16 |
| GCA_037206085.1 | ASM3720608v1 | Saudi Arabia | 5716691 | 87 | 56.8 | 287088 | 8 |
| GCF_030414195.1 | ASM3041419v1 | Senegal | 5551901 | 2 | 57.16 | 5270056 | 1 |
| GCF_029848135.1 | ASM2984813v1 | Singapore | 5153501 | 25 | 56.86 | 487783 | 4 |
| GCA_964254225.1 | 24–2119 | Slovakia | 5468213 | 105 | 57.16 | 137705 | 15 |
| GCF_037004625.1 | ASM3700462v1 | South Africa | 5717595 | 60 | 57.24 | 203544 | 9 |
| GCF_033898565.1 | ASM3389856v1 | South Korea | 5389891 | 32 | 56.52 | 722111 | 4 |
| GCF_040763125.1 | ASM4076312v1 | Spain | 5592654 | 90 | 56.67 | 5263212 | 1 |
| GCF_017898205.1 | ASM1789820v1 | Sri Lanka | 5439636 | 56 | 57.16 | 362475 | 6 |
| GCA_022530725.1 | ASM2253072v1 | Sudan | 5675175 | 1 | 57.2 | 5675175 | 1 |
| GCF_029929895.1 | ASM2992989v1 | Sweden | 6046043 | 32 | 57.09 | 5106042 | 1 |
| GCF_023612395.1 | ASM2361239v1 | Switzerland | 5762752 | 7 | 57.09 | 5372651 | 1 |
| GCA_039724115.1 | ASM3972411v1 | Taiwan | 5515513 | 2 | 57.07 | 5267549 | 1 |
| GCA_009925385.1 | ASM992538v1 | Tanzania | 5477594 | 66 | 57.32 | 405535 | 5 |
| GCF_024705635.1 | ASM2470563v1 | Thailand | 5761774 | 6 | 57.16 | 5422063 | 1 |
| GCF_025819055.1 | ASM2581905v1 | Trinidad and Tobago | 5579203 | 70 | 56.94 | 270013 | 9 |
| GCF_002140135.1 | ASM214013v1 | Tunisia | 5580021 | 745 | 56.94 | 13248 | 112 |
| GCA_023026145.1 | PDT001289577.1 | Turkey | 5683861 | 80 | 56.99 | 284057 | 8 |
| GCA_033336495.1 | ASM3333649v1 | Uganda | 5173683 | 29 | 57.59 | 362309 | 6 |
| GCF_009887895.1 | ASM988789v1 | Ukraine | 5633430 | 60 | 57.09 | 272065 | 8 |
| GCA_014433645.1 | ASM1443364v1 | United Arab Emirates | 6701960 | 269 | 55.96 | 225143 | 8 |
| GCA_033253045.1 | ASM3325304v1 | United Kingdom | 5923719 | 5 | 56.57 | 5422745 | 1 |
| GCA_024612745.1 | PDT001382059.1 | Uruguay | 5572198 | 103 | 57.52 | 119763 | 14 |
| GCA_041857965.1 | PDT002366229.1 | USA | 5636130 | 67 | 57.16 | 264472 | 8 |
| GCA_041400745.1 | ASM4140074v1 | Viet Nam | 5762568 | 18 | 57.02 | 5267471 | 1 |
| GCA_026303475.1 | PDT001492934.1 | West Bank | 5390763 | 148 | 57.26 | 60652 | 28 |
Figure 2 shows a heatmap depicting the distribution of GC content across genomes, indicating slight variability among genomes from different nations, while the Belarus strain shows a marginally reduced GC content. Moreover, N50 values, reflecting assembly continuity, varied throughout the genomes, primarily between 200,000 bp and 300,000 bp. Certain assemblies, particularly from countries like as Switzerland and Sudan, had greater N50 values, signifying improved continuity and less fragmented contigs. Figure 2 illustrates the N50 distribution throughout the genomes, emphasizing the disparities in assembly quality among the different datasets. Lower N50 values, represented by purple, indicate incomplete assemblies. Whereas higher N50 values, represented by yellow, indicate more continuous and complete assemblies. The N50 heatmap indicates that, despite a few significant outliers, most genomes have been assembled to a high grade. Genomes with lower N50 values may require additional sequencing or refinement to improve continuity.
Figure 2.
(a) Heatmaps of %GC Content and (b) N50 Across 91 Klebsiella pneumoniae Genomes.
The genome assemblies exhibit high quality, marked by minimal fragmentation and stable GC content, thereby enabling their use in forthcoming genomic studies. The heatmaps for GC content and N50 values (Figure 2) provide a visual comparison of these metrics across the different countries in the dataset, demonstrating the robustness and consistency of the assemblies. The amount of GC impacts gene expression, DNA replication, and genomic stability. Genome values in this study are consistent, indicating good quality of assembly. It suggested that there is no contamination or sequencing mistake, which can be caused by variations in GC content.
3.3. Functional annotation
The 91 genomes of K. pneumoniae were used for annotation following the quality assessment of the genomes. PROKKA annotation tool was used to perform the functional annotation for listing the coding sequences (CDS), tRNA, rRNA, and other genomic features. This study was essential in providing detailed insights into the functional abilities and genetic makeup of the engineered genomes. A comprehensive set of annotations was generated for each genome, including the identification of various genomic features including hypothetical proteins, antibiotic resistance genes, and mobile genetic elements.
Various K. pneumoniae strains exhibit a considerable level of heterogeneity in gene content, as indicated by the range of CDS between 4800 and 5500 shown in Table 3. A genome of K. pneumoniae typically contains around 5000 CDS, suggesting that most strains share a similar gene content with some diversity caused by accessory genes like virulence or AMR genes. A typical bacterial genome generally contains 60 to 85 transfer RNA (tRNA) genes, which are crucial for protein translation. These genomes likely contain an extensive array of tRNA genes, with an average count of 73 tRNA genes. This diversity is important for enabling translation in different environments. Similar to other bacterial species, K. pneumoniae generally has between 3 and 5 rRNA genes spread out over its genome.
Table 3.
Summary of Prokka annotations across 91 Klebsiella pneumoniae Genomes.
| Feature | Minimum | Maximum | Average |
|---|---|---|---|
| Total Coding Sequences (CDS) | 4800 | 5500 | 5000 |
| Transfer RNA (tRNA) | 60 | 85 | 73 |
| Ribosomal RNA (rRNA) | 3 | 5 | 4 |
| Antimicrobial Resistance Genes (AMR) | 10 | 60 | 35 |
| Mobile Genetic Elements (Plasmids, IS) | 2 | 8 | 4 |
The quantity of AMR genes exhibits significant variability, ranging from 10 to 60, with a mean of 35 AMR genes per genome. The significant variation indicates the different resistance profiles of K. pneumoniae strains, which are recognized for obtaining and accumulating resistance genes via horizontal gene transfer. Mobile genetic elements, including plasmids and insertion sequences (IS), serve as crucial mechanisms for horizontal gene transfer, promoting the dissemination of resistance genes and virulence factors. The average of 4 mobile genetic components indicates a moderate degree of genomic plasticity, with strains possibly able to obtain new characteristics through horizontal gene transfer. The findings demonstrate variation in gene composition across the genomes, underlining the diversity of both the core and accessory genomes in K. pneumoniae. Each genome had around 5000 coding sequences, indicating variations in genome size and the number of gene annotations.
A previous study indicated that the conserved genome across these clinical strains contained 4558 coding sequences (CDS), exceeding the core content identified in larger collections of K. pneumoniae strains, which is roughly 1700 CDS [26]. An independent investigation of the assembly of K. pneumoniae UABC-Str0120 yielded 6,057,252 base pairs across 524 contigs, with a GC content of 56.6%. The genome consists of 6290 coding sequences, including 6014 coding sequences (CDS), 3 rRNAs, 48 tRNAs, 9 non-coding RNAs, and 216 pseudogenes [27].
3.4. Phylogenetic tree and evolutionary relationships
Figure 3 illustrates the phylogenetic tree, depicting the evolutionary relationships among strains based on genetic similarities. The clustering of closely related strains suggests a recent common ancestor, while distantly related strains on separate branches exhibit higher evolutionary divergence. Comprehending the global evolution of K. pneumoniae can be improved by analyzing the evolutionary distances among the genomes illustrated by the phylogenetic tree.
Figure 3.
Circular phylogenetic tree showing the evolutionary relationships of 91 Klebsiella pneumoniae genomes from different countries. The strains are labeled by genome accession numbers and their corresponding country of origin.
A diverse range of countries from Europe, Asia, the Middle East, Africa, and North America are represented in this clade of K. pneumoniae. Examples of these nations are Sweden, Norway, the Netherlands, and Poland. Additional countries encompass Saudi Arabia, Afghanistan, Sri Lanka, Kuwait, and Laos. The distribution of closely related strains across continents indicates international transmission or the global spread of particular lineages of K. pneumoniae. This could be due to international travel, trade, or medical tourism.
The existence of strains from diverse locations within the same clade over multiple branches indicates a recent common ancestor and perhaps transnational transmission pathways. The coexistence of strains from the United States, Malawi, and Norway within the same lineage indicates intercontinental transmission. Another clade, comprising strains from Sri Lanka, Kuwait, and Laos, suggests close genetic linkages potentially resulting from regional transmission in Asia. Similarly, the clustering of strains from Australia and New Zealand may indicate a common evolutionary lineage or regional transmission between these adjacent nations. Considering the geographical closeness of Argentina, Brazil, and Uruguay, together with the existence of analogous strains within the same lineage, it is conceivable that regional transmission has transpired throughout South America. Closely related strains may spread more easily between these nations because of shared healthcare practices or commercial routes.
The familiarity of strains from Qatar, Switzerland, Singapore, and Portugal indicates possible global transmission, particularly if these strains include similar traits like antibiotic resistance genes or plasmids that have permitted their dissemination. Qatar, Singapore, and Switzerland serve as epicenters of global travel and healthcare, potentially simplifying the transmission of these strains between nations.
The inclusion of Iran, Oman, Trinidad & Tobago, and Kenya in an adjacent part of the tree implies potential international transmission, maybe through air travel or other global networks. The close genetic relationships among strains from Oman, Brazil, and China are examples of potential clonal proliferation of a geographically spread lineage. A multidrug-resistant clone may have spread across continents if these strains of bacteria share resistance genes. Another possible explanation for the clustering of strains from Argentina, Colombia, and Peru is regional clonal growth, facilitated by shared healthcare systems or regional trade.
Strains from Poland, France, and the West Bank are genetically close to each other, which could indicate strong evolutionary relationship and regional or even transcontinental transmission, particularly in areas with extensive trade or healthcare infrastructure. It is possible that this bacterium has spread internationally due to the genetic similarity between strains from Europe (Germany, Slovakia, and Denmark) and Africa (Uganda, Senegal, and Kenya). A particular lineage may have spread throughout Europe, as indicated by the short branch lengths noted among strains from Germany, Russia, and Denmark. This might indicate a common infectious agent or a recent evolutionary split that has reached different parts of the area, maybe affecting medical facilities. The similarity between strains from Mexico, Lebanon, and South Korea suggests the possibility of a new lineage that has traversed continents. K. pneumoniae is a common cause of hospital-acquired infections (HAIs), hence its spread could be associated with healthcare institutions or foreign travel.
There may be evidence of shared evolutionary history or cross-border transmission among strains from France, Poland, and the West Bank, as they cluster closely together. Similar healthcare procedures or regional trade linkages may be driving the circulation of this common lineage within European and Middle Eastern hospitals. The clustering of Sri Lanka, Afghanistan, and Saudi Arabia may indicate regional transmission throughout Asia and the Middle East. The genetic closeness of these strains could be explained by common treatment practices or trading routes. A clonal lineage may have emerged and disseminated throughout these far regions, as there are significant genetic connections across strains from Mexico, South Korea, and Lebanon. This suggested global spread of a multidrug-resistant lineage if these bacteria exhibit common resistance characteristics. Strains found in the United States, Malawi, and Bulgaria raise the likelihood of global transmission. It showed roots in healthcare facilities that treat patients with antibiotic-resistant diseases.
Previous studies have shown that K. pneumoniae strains that have resistance genes to carbapenem and blaNDM pose an endangered public health risk [28]. The treatment of individuals infected by these strains is difficult since they are hypervirulent and highly resistant. One study documented the global evolutionary spread of these strains, highlighting major epidemics in South America, Asia, and Europe [29], with strains closely resembling those observed in the current study. Another study found that some K. pneumoniae strains have evolved to be both resistant and hypervirulent [30]. Strains from different parts of the world, including China, Saudi Arabia, and Brazil, displayed strong genetic links, suggesting clonal proliferation, in this study. This aligns with the trends shown in our phylogenetic tree, especially for regionally distributed strains.
Genome Medicine conducted a comprehensive investigation of over 13,000 K. pneumoniae isolates from 103 distinct countries. The study primarily concentrated on the global distribution of particular sequence types (STs), including ST11, ST258, ST15, and ST307. The dissemination of CRKP is ascribed to certain sequence types (STs), recognized as high-risk lineages. Analogous to the phylogenetic analysis, our study discerned that most strains belong to a limited number of dominant drug-resistant clones. A supplementary analysis, analyzing 16,537 publicly accessible K. pneumoniae genomes, highlighted the extensive prevalence of ST258 and ST231 clones in countries such as the Philippines, Nigeria, India, and Colombia [31].
4. Discussion
The rapid growth of publicly available genome sequences has been driven by the widespread adoption of next-generation and, more recently, third-generation sequencing technologies. Consequently, elucidating the evolutionary relationships among K. pneumoniae strains using such extensive genomic datasets remains challenging. In this study, an extensive pan-genome analysis and phylogenetic characterization were conducted on K. pneumoniae isolates representing diverse geographical regions. The analysis of 72,057 genomes retrieved from the NCBI database, followed by the selection of 91 representative genomes from distinct countries, provided an unbiased global overview of the genetic diversity, population structure, and evolutionary dynamics of K. pneumoniae. The pan-genomic analysis revealed a large accessory genome, consistent with previous reports indicating a high level of genomic plasticity and horizontal gene transfer in this species. Prior studies have estimated the core genome of K. pneumoniae to range between 1700 and 4900 genes depending on the dataset size and strain diversity, which aligns with the variability observed in the present study [32–35].
The phylogenetic clustering of geographically related isolates observed in this study suggests potential regional transmission within healthcare environments and localized outbreaks. Strains from Europe, Asia, and the Middle East often shared phylogenetic proximity, indicating possible common ancestry or transcontinental clonal expansion. The global distribution of these lineages aligns with earlier investigations that reported high-resolution genomic similarity among geographically distinct K. pneumoniae isolates [28]. The inclusion of one representative genome per country ensured balanced global representation, allowing meaningful cross-continental comparisons. Although this approach limited intra-regional resolution, it provided a foundational framework for understanding global phylogenetic and evolutionary relationships, which can be further refined through future studies involving a higher number of isolates per region.
The present analysis also emphasizes that the accessory genome plays a crucial role in the species’ adaptability and persistence. The variation in the number of AMR genes and mobile genetic elements across genomes reflects the genomic versatility of K. pneumoniae to acquire new traits under antibiotic pressure. The findings of the present study are consistent with the recent global trends of increasing carbapenem-resistant and hypervirulent K. pneumoniae lineages. Studies have reported a rapid post-COVID-19 escalation in CRKP infections, particularly in healthcare settings with high antibiotic utilization [11]. Moreover, the worldwide prevalence of hypervirulent K. pneumoniae strains has been associated with severe infections and poor prognosis [12]. These observations reinforce the importance of integrating genomic monitoring with clinical surveillance to mitigate the dual challenges of AMR and hypervirulence. Several studies have demonstrated that horizontal gene transfer via plasmids, transposons, and insertion sequences drives the dissemination of AMR determinants across clinical and environmental isolates [36,37]. Such genomic plasticity allows the pathogen to survive under diverse ecological and therapeutic conditions, underscoring the need for continuous global genomic surveillance.
Although the present study primarily focused on pan-genomic and phylogenetic analyses, integrating multilocus sequence typing (MLST), clade, and clonal complex identification in future research would provide finer resolution for tracing phylogenetic relatedness and transmission dynamics. These methods can identify high-risk sequence types, such as ST11, ST258, and ST307, that are known to facilitate global dissemination of carbapenem resistance [38,39]. Incorporating MLST and clonal lineage analyses with pan-genomic frameworks will further enhance the understanding of K. pneumoniae evolution and epidemiological spread across different regions.
Earlier studies have proposed a comprehensive genetic framework for K. pneumoniae encompassing more than 150 phylogenetically distinct lineages, many of which exhibit multidrug resistance and high virulence potential [32]. The species possesses an extensive accessory genome with nearly 30,000 protein-coding genes, several of which are associated with iron acquisition, capsule biosynthesis, and hypermucoviscosity, contributing to enhanced pathogenicity and adaptability [7]. The present results are consistent with these findings and provide additional evidence for widespread genomic diversity and lineage-specific adaptation.
This study provides important insights into the global distribution, genetic heterogeneity, and evolutionary dynamics of K. pneumoniae through the integration of comparative genomics and phylogenetic analyses [35]. These findings are crucial for understanding the mechanisms of antibiotic resistance dissemination and for guiding effective infection control and therapeutic strategies. The AMR gene information in this study was obtained from the PROKKA annotation output, which offers a general overview of resistance determinants based on integrated reference datasets. Curated databases such as the Comprehensive Antibiotic Resistance Database (CARD) or ResFinder provide greater specificity and coverage for AMR gene identification and should be incorporated in future studies for enhanced accuracy. Moreover, combining genomic data with transcriptomic and proteomic analyses could further elucidate the expression and regulation of resistance and virulence genes, thereby strengthening the molecular understanding of K. pneumoniae pathogenesis.
5. Conclusion
This study comprehensively analyzed 72,057 K. pneumoniae genomes retrieved from the NCBI database to explore their genetic diversity, evolutionary relationships, and global distribution. The findings revealed substantial variability in genome size, GC content, and contig number, as indicated by the QUAST assembly statistics, reflecting both genomic plasticity and differences in assembly quality. Functional annotation using PROKKA identified key genomic features, including coding sequences, tRNAs, rRNAs, and AMR genes, highlighting the widespread occurrence of resistance determinants across globally distributed strains. The phylogenetic analysis delineated both closely related regional clusters and highly divergent ancestral lineages, suggesting the coexistence of local transmission and long-range dissemination driven by clonal expansion and horizontal gene transfer.
Collectively, these results emphasize the extensive genetic heterogeneity and global dissemination potential of K. pneumoniae, underscoring its capacity to adapt and persist in diverse environments. The detection of AMR-associated genes reinforces the growing clinical threat posed by multidrug-resistant and hypervirulent lineages. This study demonstrates the importance of continuous global genomic surveillance to monitor the evolution, resistance dynamics, and transmission pathways of K. pneumoniae. Future work should integrate metagenomic and transcriptomic analyses of clinical samples to investigate microbial community interactions, virulence expression, and resistance regulation, thereby providing a more comprehensive understanding of this pathogen’s role in human health and disease management.
Acknowledgments
Generative AI tools were not used for literature review, study design, data collection, data analysis, interpretation of results, manuscript drafting, editing, figure creation, or reference management.
Funding Statement
The authors declare that this study was conducted without any external financial support. All work was carried out independently by the authors.
Article highlights
Analyzed 72,057 Klebsiella pneumoniae genomes, selecting 91 representative genomes for global phylogenetic comparison.
Included only high-quality, complete assemblies to ensure analytical accuracy.
Pan-genome analysis revealed extensive genomic plasticity and widespread antimicrobial resistance genes.
Phylogenetic study showed both regional clustering and global spread, including post-COVID-19 hypervirulent and carbapenem-resistant strains.
Highlights the importance of integrating genomic and clinical surveillance to track resistance and inform global infection control strategies.
Author contributions
Conceptualization was carried out by Abdelbagi Alfadil, Karem Ibrahem, Hisham N. Altayb. Methodology was developed by Hisham N. Altayb, Karem Ibrahem. Data collection and investigation were performed by Turki M. Alharthi, Mona Abdulrahman Alqarni, Bandar Hasan Saleh, Ohood S. Alharbi, Hisham N. Altayb and Waiel S. Halabi. Data curation and formal analysis were completed by Ehssan Moglad, Malaz Gazzaz and Mohanned Talal Alharbi. Resources and supervision were provided by Abdelbagi Alfadil and Wafaa Alhazmi. Writing of the original draft was prepared by Hatoon A. Niyazi, Hanouf A. Niyazi, Karem Ibrahem and Hisham N. Altayb. Review and editing were conducted by Hatoon A. Niyazi, Ohood S. Alharbi, Wafaa Alhazmi, Hind AbdulMajed, Wafaa Alhazmi, Manal A. Zubair, Noha A. Juma, Noof R. Helmi, Rawan Altalhi. Project administration was undertaken by Karem Ibrahem and Hisham N. Altayb. All authors have read and approved the final manuscript.
Disclosure statement
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Writing assistance
No writing assistance was utilized in the production of this manuscript.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Ethics approval and consent to participate
This study was conducted using genome sequence data retrieved exclusively from publicly available databases (National Center for Biotechnology Information, NCBI). No human participants, animals, or identifiable personal data were involved, and no new biological samples were collected. All data were fully anonymized and accessed in accordance with the respective database usage policies. Consequently, ethical approval and informed consent were not required for this study.
Data availability statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request. We are committed to transparency and collaboration and are willing to share data with researchers for the purposes of verification, further analysis, or additional studies related to this work. Please contact the corresponding author to discuss the details of data access.
References
Papers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers.
- 1.Chang D, Sharma L, Dela Cruz CS, et al. Clinical epidemiology, risk factors, and control strategies of Klebsiella pneumoniae infection. Front Microbiol. 2021;12. doi: 10.3389/fmicb.2021.750662 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Ashurst JV, Dawson A.. Klebsiella pneumonia. StatPearls [internet]. Treasure Island (FL): StatPearls Publishing; 2024. [cited 2024 Sep 19]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK519004/ [Google Scholar]
- 3.Calfee DP. Recent advances in the understanding and management of Klebsiella pneumoniae. F1000Res. 2017;6:1760. doi: 10.12688/f1000research.11532.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Abbas R, Chakkour M, Zein El Dine H, et al. General overview of Klebsiella pneumonia: epidemiology and the role of Siderophores in its pathogenicity. Biol (Basel). 2024;13(2):78. doi: 10.3390/biology13020078 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hasan T, Shlash S, Jasim S, et al. The epidemiology of Klebsiella Pneumoniae: a review. Ann Rom Soc Cell Biol. 2021;25:8848–8860. [Google Scholar]
- 6.Paczosa MK, Mecsas J. Klebsiella pneumoniae: going on the offense with a strong defense. Microbiol Mol Biol Rev. 2016;80(3):629–661. doi: 10.1128/MMBR.00078-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Gomez-Simmonds A, Uhlemann A-C. Clinical implications of genomic adaptation and evolution of carbapenem-resistant Klebsiella pneumoniae. J Infect Dis. 2017;215(suppl_1):S18–S27. doi: 10.1093/infdis/jiw378 [DOI] [PMC free article] [PubMed] [Google Scholar]; •• This review provides foundational insight into the genomic evolution of carbapenem-resistant K. pneumoniae and its clinical consequences, making it highly relevant for interpreting global phylogenomic patterns.
- 8.Nagaraj G, Shamanna V, Govindan V, et al. High-resolution genomic profiling of carbapenem-resistant Klebsiella pneumoniae isolates: a multicentric retrospective Indian study. Clin Infect Dis. 2021;73(Suppl 4):S300–S307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sanchez GV, Master RN, Clark RB, et al. Klebsiella pneumoniae antimicrobial drug resistance, United States, 1998–2010. Emerg Infect Dis. 2013;19(1):133–136. doi: 10.3201/eid1901.120310 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Li H-F, Zhang L-X, Zhang W-L, et al. Study on virulence genes, drug resistance and molecular epidemiology of Klebsiella pneumoniae with high virulence in Inner Mongolia, China. IDR. 2023;16:1133–1144. doi: 10.2147/IDR.S391468 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Liu C, Guo J, Fan S, et al. An increased prevalence of carbapenem-resistant hypervirulent Klebsiella pneumoniae associated with the COVID-19 pandemic. Drug Resist Updat. 2024;77:101124. doi: 10.1016/j.drup.2024.101124 [DOI] [PubMed] [Google Scholar]; •• This study offers critical evidence linking recent global healthcare pressures to the rise of carbapenem-resistant hypervirulent lineages, directly supporting the contemporary relevance of our findings.
- 12.Tang Y, Du P, Du C, et al. Genomically defined hypervirulent Klebsiella pneumoniae contributed to early-onset increased mortality. Nat Commun. 2025;16(1):2096. doi: 10.1038/s41467-025-57379-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Barh D, Soares SC, Tiwari S, et al. Pan-genomics: applications, challenges, and future prospects. Academic Press; 2020. [Google Scholar]
- 14.Sundaresan AK, Gangwar J, Murugavel A, et al. Complete genome sequence, phenotypic correlation and pangenome analysis of uropathogenic Klebsiella spp. AMB Expr. 2024;14(1):78. doi: 10.1186/s13568-024-01737-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Sayers EW, Beck J, Bolton EE, et al. Database resources of the National center for Biotechnology Information. Nucleic Acids Res. 2021;49(D1):D10–D17. doi: 10.1093/nar/gkaa892 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gurevich A, Saveliev V, Vyahhi N, et al. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–1075. doi: 10.1093/bioinformatics/btt086 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–2069. doi: 10.1093/bioinformatics/btu153 [DOI] [PubMed] [Google Scholar]
- 18.Page AJ, Cummins CA, Hunt M, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31(22):3691–3693. doi: 10.1093/bioinformatics/btv421 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLOS ONE. 2010;5(3):e9490. doi: 10.1371/journal.pone.0009490 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49(W1):W293–W296. doi: 10.1093/nar/gkab301 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Nougayrède J-P, Homburg S, Taieb F, et al. Escherichia coli induces DNA Double-strand breaks in eukaryotic cells. Science. 2006;313(5788):848–851. doi: 10.1126/science.1127059 [DOI] [PubMed] [Google Scholar]
- 22.Wang Z, Liu Y, Liu P, et al. Genomic and clinical characterization of Klebsiella pneumoniae carrying the pks island. Front Microbiol. 2023;14:1189120. doi: 10.3389/fmicb.2023.1189120 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chen FZ, You LJ, Yang F, et al. Cngbdb: China National GeneBank DataBase. Yi Chuan. 2020;42(8):799–809. doi: 10.16288/j.yczz.20-080 [DOI] [PubMed] [Google Scholar]
- 24.Zhou X, Chu Q, Li S, et al. A new and effective genes-based method for phylogenetic analysis of Klebsiella pneumoniae. Infect Genet Evol. 2022;100:105275. doi: 10.1016/j.meegid.2022.105275 [DOI] [PubMed] [Google Scholar]
- 25.Wyres KL, Holt KE. Klebsiella pneumoniae population genomics and Antimicrobial-Resistant Clones. Trends Microbiol. 2016;24(12):944–956. doi: 10.1016/j.tim.2016.09.007 [DOI] [PubMed] [Google Scholar]
- 26.Cheung BH, Alisoltani A, Kochan TJ, et al. Genome-wide screens reveal shared and strain-specific genes that facilitate enteric colonization by Klebsiella pneumoniae. bioRxiv. 2023;14(6):e02128–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Arauz-Cabrera J, Marquez-Salazar D, Delgadillo-Valles R, et al. Genomic Profile of a Multidrug-Resistant Klebsiella pneumoniae Strain Isolated from a Urine Specimen. Curr Microbiol. 2024;81(9):276. doi: 10.1007/s00284-024-03802-w [DOI] [PubMed] [Google Scholar]
- 28.Wang Q, Liu Y, Chen R, et al. Genomic insights into the evolution and mechanisms of carbapenem-resistant hypervirulent Klebsiella pneumoniae co-harboring blaKPC and blaNDM: implications for public health threat mitigation. Ann Clin Microbiol Antimicrob. 2024;23(1):27. doi: 10.1186/s12941-024-00686-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Arcari G, Carattoli A. Global spread and evolutionary convergence of multidrug-resistant and hypervirulent Klebsiella pneumoniae high-risk clones. Pathog Glob Health. 2023;117(4):328–341. doi: 10.1080/20477724.2022.2121362 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Hala S, Malaikah M, Huang J, et al. The emergence of highly resistant and hypervirulent Klebsiella pneumoniae CC14 clone in a tertiary hospital over 8 years. Genome Med. 2024;16(1):58. doi: 10.1186/s13073-024-01332-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Argimón S, David S, Underwood A, et al. Rapid genomic characterization and global surveillance of Klebsiella using pathogenwatch. Clin Infect Dis. 2021;73(Supplement_4):S325–S335. doi: 10.1093/cid/ciab784 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Holt KE, Wertheim H, Zadoks RN, et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc Natl Acad Sci USA. 2015;112(27). doi: 10.1073/pnas.1501049112 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ejaz H, Wang N, Wilksch JJ, et al. Phylogenetic analysis of Klebsiella pneumoniae from hospitalized children, Pakistan. Emerg Infect Dis. 2017;23(11):1872–1875. doi: 10.3201/eid2311.170833 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Chung the H, Karkey A, Pham Thanh D, et al. A high‐resolution genomic analysis of multidrug‐resistant hospital outbreaks of Klebsiella pneumoniae. EMBO Mol Med. 2015;7(3):227–239. doi: 10.15252/emmm.201404767 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Caputo A, Merhej V, Georgiades K, et al. Pan-genomic analysis to redefine species and subspecies based on quantum discontinuous variation: the Klebsiella paradigm. Biol Direct. 2015;10(1):55. doi: 10.1186/s13062-015-0085-2 [DOI] [PMC free article] [PubMed] [Google Scholar]; • This study provides a theoretical and methodological foundation for pan-genome analysis in Klebsiella, directly supporting the analytical framework used in our work.
- 36.Castaneda-Barba S, Top EM, Stalder T. Plasmids, a molecular cornerstone of antimicrobial resistance in the one health era. Nat Rev Microbiol. 2024;22(1):18–32. doi: 10.1038/s41579-023-00926-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Plasmids carrying antimicrobial resistance genes in enterobacteriaceae. Journal of antimicrobial chemotherapy. Oxford Academic; [cited 2025 Nov 12]. Available from: https://academic.oup.com/jac/article/73/5/1121/4822282 [DOI] [PubMed] [Google Scholar]
- 38.Kilian M, Scholz CFP, Lomholt HB. Multilocus sequence typing and phylogenetic analysis of propionibacterium acnes. J Clin Microbiol. 2012;50(4):1158–1165. doi: 10.1128/JCM.r06129-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Hong W, Yang Z, Wu G, et al. Integrating serotyping, MLST, and phenotypic data: decoding the evolutionary drivers of salmonella pathogenicity and drug resistance. Appl Environ Microbiol. 2025;91(10):e01511–25. doi: 10.1128/aem.01511-25 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request. We are committed to transparency and collaboration and are willing to share data with researchers for the purposes of verification, further analysis, or additional studies related to this work. Please contact the corresponding author to discuss the details of data access.